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dc.contributor.authorPuraivan, E.
dc.contributor.authorGodoy, E.
dc.contributor.authorRiquelme, F.
dc.contributor.authorSalas, R.
dc.description.abstractOnline social networks are a powerful communication and information dissemination tool, particularly useful in complex scenarios such as social crises, natural disasters, and pandemics. However, one of the main problems, especially in socio-political crises, is the automatic detection of fake news. This problem is usually addressed with greater or lesser success using supervised machine learning techniques. In this work, we propose a mixed approach, using unsupervised learning for feature extraction, and supervised learning for the prediction of fake news on microblogging networks. We consider Twitter news with linguistic and network features. To identify hidden patterns in the data, we use Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding. The results show that the data can be better classified using non-linear rather than linear separability. Moreover, when using Extreme Gradient Boosting (XGBoost), an accuracy of 99.26% is obtained, and the most relevant features are identified.es_ES
dc.source11th International Conference of Pattern Recognition Systems (ICPRS 2021); 157 - 162es_ES
dc.subjectFake newses_ES
dc.subjectSocial crisises_ES
dc.subjectExplainable machine learninges_ES
dc.subjectData mininges_ES
dc.titleFake news detection on Twitter using a data mining framework based on explainable machine learning techniqueses_ES
dc.typeArtículo de revistaes_ES
uvm.escuelaEscuela de Educaciónes_ES

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